{"cells": [{"metadata": {}, "cell_type": "markdown", "source": "# 0.\u6570\u636e\u96c6\u4ecb\u7ecd\u4e0e\u5b9e\u9a8c\u51c6\u5907\n\n## \u6570\u636e\u96c6\u4ecb\u7ecd\n\u968f\u7740\u793e\u4ea4\u996e\u9152\u7684\u589e\u52a0\uff0c\u8461\u8404\u9152\u884c\u4e1a\u9500\u91cf\u6301\u7eed\u589e\u957f\u3002\u8461\u8404\u9152\u7684\u4ef7\u683c\u53d6\u51b3\u4e8e\u8461\u8404\u9152\u54c1\u5c1d\u8005\u5bf9\u8461\u8404\u9152\u6b23\u8d4f\u7684\u4e00\u4e2a\u76f8\u5f53\u62bd\u8c61\u7684\u6982\u5ff5\uff0c\u5176\u4e2d\u7684\u89c2\u70b9\u53ef\u80fd\u5177\u6709\u9ad8\u5ea6\u7684\u53ef\u53d8\u6027\u3002\u8461\u8404\u9152\u7684\u5b9a\u4ef7\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u53d6\u51b3\u4e8e\u8fd9\u79cd\u4e3b\u89c2\u7684\u56e0\u7d20\u3002\u8461\u8404\u9152\u8ba4\u8bc1\u548c\u8d28\u91cf\u8bc4\u4f30\u7684\u53e6\u4e00\u4e2a\u5173\u952e\u56e0\u7d20\u662f\u57fa\u4e8e\u5b9e\u9a8c\u5ba4\u7684\u7269\u7406\u5316\u5b66\u6d4b\u8bd5\uff0c\u5e76\u8003\u8651\u4e86\u9178\u5ea6\uff0cpH\u503c\uff0c\u7cd6\u7684\u5b58\u5728\u548c\u5176\u4ed6\u5316\u5b66\u6027\u8d28\u7b49\u56e0\u7d20\u3002\n\n\u8461\u8404\u9152\u5e02\u573a\u73b0\u5728\u4e3b\u8981\u6709\u4e24\u4e2a\u6570\u636e\u96c6\uff0c\u5176\u4e2d\u4e00\u4e2a\u6570\u636e\u96c6\u662f\u7ea2\u8461\u8404\u9152\u6570\u636e\u96c6\uff0c\u67091599\u4e2a\u6837\u4f8b\uff0c\u53e6\u4e00\u4e2a\u662f\u767d\u8461\u8404\u9152\u6570\u636e\u96c6\uff0c\u67094898\u4e2a\u6837\u4f8b\u3002\u6b64\u6b21\u6837\u4f8b\u4ec5\u5206\u6790\u7ea2\u8461\u8404\u9152\u6570\u636e\u3002\u6240\u6709\u8461\u8404\u9152\u5747\u5728\u8461\u8404\u7259\u7684\u7279\u5b9a\u5730\u533a\u751f\u4ea7\u3002\u6536\u96c6\u4e86\u8461\u8404\u9152\u768412\u79cd\u4e0d\u540c\u7279\u6027\u7684\u6570\u636e\uff0c\u5176\u4e2d\u4e00\u79cd\u662f\u8d28\u91cf\uff0c\u5176\u4f59\u7684\u662f\u8461\u8404\u9152\u7684\u5316\u5b66\u7279\u6027\uff0c\u5305\u62ec\u5bc6\u5ea6\uff0c\u9178\u5ea6\uff0c\u9152\u7cbe\u542b\u91cf\u7b49\u3002\u8461\u8404\u9152\u7684\u6240\u6709\u5316\u5b66\u7279\u6027\u90fd\u662f\u8fde\u7eed\u53d8\u91cf\u3002\u8d28\u91cf\u662f\u4e00\u4e2a\u6709\u5e8f\u53d8\u91cf\uff0c\u53ef\u80fd\u4ece1\uff08\u6700\u5dee\uff09\u523010\uff08\u6700\u4f73\uff09\u6392\u540d\u3002\u6bcf\u79cd\u8461\u8404\u9152\u90fd\u7531\u4e09\u4e2a\u72ec\u7acb\u54c1\u5c1d\u8005\u54c1\u5c1d\uff0c\u6700\u7ec8\u5206\u6570\u662f\u7531\u4e0d\u540c\u54c1\u5c1d\u8005\u7ed9\u51fa\u7684\u5206\u6570\u4e2d\u53d6\u5f97\u4e2d\u4f4d\u6570\u3002\n\n\u8bfb\u8005\u4e5f\u53ef\u4ee5\u6839\u636e\u672c\u6b21\u6837\u4f8b\u7684\u5206\u6790\u65b9\u6cd5\u5206\u6790\u767d\u8461\u8404\u9152\u7279\u6027\u4e0e\u8d28\u91cf\u7684\u5173\u7cfb\u5e76\u9884\u6d4b\u767d\u8461\u8404\u6570\u636e\u96c6\u7684\u8d28\u91cf\u3002"}, {"metadata": {}, "cell_type": "markdown", "source": "## \u5b9e\u9a8c\u51c6\u5907\n\n### \u8fdb\u5165ModelArts\u754c\u9762\n\n\u70b9\u51fb\u5982\u4e0b\u94fe\u63a5\uff1ahttps://www.huaweicloud.com/product/modelarts.html \uff0c\u8fdb\u5165ModelArts\u4e3b\u9875\u3002\u70b9\u51fb\u201c\u7acb\u5373\u4f7f\u7528\u201d\u6309\u94ae\uff0c\u8f93\u5165\u7528\u6237\u540d\u548c\u5bc6\u7801\u767b\u5f55\uff0c\u8fdb\u5165ModelArts\u4f7f\u7528\u9875\u9762\u3002\n\n### \u521b\u5efaModelArts Notebook\n\n\u4e0b\u9762\uff0c\u6211\u4eec\u5728ModelArts\u4e2d\u521b\u5efa\u4e00\u4e2anotebook\u5f00\u53d1\u73af\u5883\uff0cModelArts notebook\u63d0\u4f9b\u7f51\u9875\u7248\u7684Python\u5f00\u53d1\u73af\u5883\uff0c\u53ef\u4ee5\u65b9\u4fbf\u7684\u7f16\u5199\u3001\u8fd0\u884c\u4ee3\u7801\uff0c\u5e76\u67e5\u770b\u8fd0\u884c\u7ed3\u679c\u3002\n\n\u7b2c\u4e00\u6b65\uff1a\u5728ModelArts\u670d\u52a1\u4e3b\u754c\u9762\u4f9d\u6b21\u70b9\u51fb\u201c\u5f00\u53d1\u73af\u5883\u201d\u3001\u201c\u521b\u5efa\u201d\n\n![create_nb_create_button](./img/create_nb_create_button.png)\n\n\u7b2c\u4e8c\u6b65\uff1a\u586b\u5199notebook\u6240\u9700\u7684\u53c2\u6570\uff1a\n\n| \u53c2\u6570 | \u8bf4\u660e |\n| - - - - - | - - - - - |\n| \u8ba1\u8d39\u65b9\u5f0f | \u6309\u9700\u8ba1\u8d39  |\n| \u540d\u79f0 | Notebook\u5b9e\u4f8b\u540d\u79f0\uff0c\u5982 kmeans_customer_segmentation |\n| \u5de5\u4f5c\u73af\u5883 | Python3 |\n| \u8d44\u6e90\u6c60 | \u9009\u62e9\"\u516c\u5171\u8d44\u6e90\u6c60\"\u5373\u53ef |\n| \u7c7b\u578b | CPU |\n| \u89c4\u683c | 2\u68388GiB |\n| \u5b58\u50a8\u914d\u7f6e | \u9009\u62e9EVS\uff0c\u78c1\u76d8\u89c4\u683c5GB |\n\n\u7b2c\u4e09\u6b65\uff1a\u914d\u7f6e\u597dnotebook\u53c2\u6570\u540e\uff0c\u70b9\u51fb\u4e0b\u4e00\u6b65\uff0c\u8fdb\u5165notebook\u4fe1\u606f\u9884\u89c8\u3002\u786e\u8ba4\u65e0\u8bef\u540e\uff0c\u70b9\u51fb\u201c\u7acb\u5373\u521b\u5efa\u201d\n\n![create_nb_creation_summary](./img/create_nb_creation_summary.png)\n\n\u7b2c\u56db\u6b65\uff1a\u521b\u5efa\u5b8c\u6210\u540e\uff0c\u8fd4\u56de\u5f00\u53d1\u73af\u5883\u4e3b\u754c\u9762\uff0c\u7b49\u5f85Notebook\u521b\u5efa\u5b8c\u6bd5\u540e\uff0c\u6253\u5f00Notebook\uff0c\u8fdb\u884c\u4e0b\u4e00\u6b65\u64cd\u4f5c\u3002\n![modelarts_notebook_index](./img/modelarts_notebook_index.png)\n\n### \u5728ModelArts\u4e2d\u521b\u5efa\u5f00\u53d1\u73af\u5883\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u5b9e\u9645\u7684\u5f00\u53d1\u73af\u5883\uff0c\u7528\u4e8e\u540e\u7eed\u7684\u5b9e\u9a8c\u6b65\u9aa4\u3002\n\n\u7b2c\u4e00\u6b65\uff1a\u70b9\u51fb\u4e0b\u56fe\u6240\u793a\u7684\u201c\u6253\u5f00\u201d\u6309\u94ae\uff0c\u8fdb\u5165\u521a\u521a\u521b\u5efa\u7684Notebook\n![inter_dev_env](img/enter_dev_env.png)\n\n\u7b2c\u4e8c\u6b65\uff1a\u521b\u5efa\u4e00\u4e2aPython3\u73af\u5883\u7684\u7684Notebook\u3002\u70b9\u51fb\u53f3\u4e0a\u89d2\u7684\"New\"\uff0c\u7136\u540e\u521b\u5efa XGBoost-Sklearn \u5f00\u53d1\u73af\u5883\u3002\n\n\u7b2c\u4e09\u6b65\uff1a\u70b9\u51fb\u5de6\u4e0a\u65b9\u7684\u6587\u4ef6\u540d\"Untitled\"\uff0c\u5e76\u8f93\u5165\u4e00\u4e2a\u4e0e\u672c\u5b9e\u9a8c\u76f8\u5173\u7684\u540d\u79f0\uff0c\u5982\"wine-quality-regresiion\"\n![notebook_untitled_filename](./img/notebook_untitled_filename.png)\n![notebook_name_the_ipynb](./img/notebook_name_the_ipynb.png)\n\n\n### \u5728Notebook\u4e2d\u7f16\u5199\u5e76\u6267\u884c\u4ee3\u7801\n\n\u5728Notebook\u4e2d\uff0c\u6211\u4eec\u8f93\u5165\u4e00\u4e2a\u7b80\u5355\u7684\u6253\u5370\u8bed\u53e5\uff0c\u7136\u540e\u70b9\u51fb\u4e0a\u65b9\u7684\u8fd0\u884c\u6309\u94ae\uff0c\u53ef\u4ee5\u67e5\u770b\u8bed\u53e5\u6267\u884c\u7684\u7ed3\u679c\uff1a\n![run_helloworld](./img/run_helloworld.png)\n\n\n\u5f00\u53d1\u73af\u5883\u51c6\u5907\u597d\u5566\uff0c\u63a5\u4e0b\u6765\u53ef\u4ee5\u6109\u5feb\u5730\u5199\u4ee3\u7801\u5566\uff01\n"}, {"metadata": {}, "cell_type": "markdown", "source": "## \u5bfc\u5165\u7c7b\u5e93"}, {"metadata": {"scrolled": true, "trusted": true}, "cell_type": "code", "source": "import csv\nimport os\nimport numpy as np\nfrom numpy import allclose\nimport sklearn\nfrom sklearn.model_selection import train_test_split\nimport random\nimport requests\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom operator import itemgetter\nimport pyspark\nfrom pyspark.ml.linalg import Vectors\nfrom pyspark import SparkContext \nfrom pyspark.sql import SQLContext \nfrom pyspark.sql.types import StructType,StringType,StructField\nfrom pyspark.ml.regression import LinearRegression\nfrom pyspark.ml.feature import *\nfrom pyspark.ml import Pipeline, PipelineModel\nfrom pyspark.ml.evaluation import RegressionEvaluator\nfrom pyspark.ml.regression import GeneralizedLinearRegression\nfrom pyspark.ml.regression import DecisionTreeRegressor\nfrom pyspark.ml.regression import RandomForestRegressor\nfrom pyspark.ml.regression import GBTRegressor\nfrom pyspark.ml.regression import AFTSurvivalRegression\nfrom pyspark.ml.stat import Correlation\nimport requests \n\n", "execution_count": 118, "outputs": []}, {"metadata": {}, "cell_type": "markdown", "source": "# 1.\u6570\u636e\u9884\u5904\u7406"}, {"metadata": {}, "cell_type": "markdown", "source": "## 1.1\u4e0b\u8f7d\u6570\u636e\u5e76\u663e\u793a\u524d5\u884c\n\u672c\u6837\u4f8b\u6570\u636e\u5df2\u7ecf\u9884\u5148\u88ab\u4e0a\u4f20\u5230\u516c\u5171OBS\u6876\u91cc\uff0c\u6211\u4eec\u53ea\u9700\u8981\u4e0b\u8f7d\u8bfb\u53d6\u5373\u53ef"}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "url = 'https://modelarts-labs.obs.cn-north-1.myhuaweicloud.com/notebook/ML_red_wine_quality/winequality-red.csv'\nr = requests.get(url) \nwith open(\"./winequality-red.csv\", \"wb\") as code:\n    code.write(r.content)\ndata=  pd.read_csv(\"./winequality-red.csv\")\ndata.head(5)", "execution_count": 141, "outputs": [{"output_type": "execute_result", "execution_count": 141, "data": {"text/plain": "   numbers  volatile acidity  fixed acidity  citric acid  residual sugar  \\\n0        0              0.70            7.4         0.00             1.9   \n1        1              0.88            7.8         0.00             2.6   \n2        2              0.76            7.8         0.04             2.3   \n3        3              0.28           11.2         0.56             1.9   \n4        4              0.70            7.4         0.00             1.9   \n\n   chlorides  free sulfur dioxide  total sulfur dioxide  density    pH  \\\n0      0.076                 11.0                  34.0   0.9978  3.51   \n1      0.098                 25.0                  67.0   0.9968  3.20   \n2      0.092                 15.0                  54.0   0.9970  3.26   \n3      0.075                 17.0                  60.0   0.9980  3.16   \n4      0.076                 11.0                  34.0   0.9978  3.51   \n\n   sulphates  alcoho  quality  \n0       0.56     9.4        5  \n1       0.68     9.8        5  \n2       0.65     9.8        5  \n3       0.58     9.8        6  \n4       0.56     9.4        5  ", "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>numbers</th>\n      <th>volatile acidity</th>\n      <th>fixed acidity</th>\n      <th>citric acid</th>\n      <th>residual sugar</th>\n      <th>chlorides</th>\n      <th>free sulfur dioxide</th>\n      <th>total sulfur dioxide</th>\n      <th>density</th>\n      <th>pH</th>\n      <th>sulphates</th>\n      <th>alcoho</th>\n      <th>quality</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.70</td>\n      <td>7.4</td>\n      <td>0.00</td>\n      <td>1.9</td>\n      <td>0.076</td>\n      <td>11.0</td>\n      <td>34.0</td>\n      <td>0.9978</td>\n      <td>3.51</td>\n      <td>0.56</td>\n      <td>9.4</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.88</td>\n      <td>7.8</td>\n      <td>0.00</td>\n      <td>2.6</td>\n      <td>0.098</td>\n      <td>25.0</td>\n      <td>67.0</td>\n      <td>0.9968</td>\n      <td>3.20</td>\n      <td>0.68</td>\n      <td>9.8</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>0.76</td>\n      <td>7.8</td>\n      <td>0.04</td>\n      <td>2.3</td>\n      <td>0.092</td>\n      <td>15.0</td>\n      <td>54.0</td>\n      <td>0.9970</td>\n      <td>3.26</td>\n      <td>0.65</td>\n      <td>9.8</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>0.28</td>\n      <td>11.2</td>\n      <td>0.56</td>\n      <td>1.9</td>\n      <td>0.075</td>\n      <td>17.0</td>\n      <td>60.0</td>\n      <td>0.9980</td>\n      <td>3.16</td>\n      <td>0.58</td>\n      <td>9.8</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>0.70</td>\n      <td>7.4</td>\n      <td>0.00</td>\n      <td>1.9</td>\n      <td>0.076</td>\n      <td>11.0</td>\n      <td>34.0</td>\n      <td>0.9978</td>\n      <td>3.51</td>\n      <td>0.56</td>\n      <td>9.4</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}]}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "data=data.drop(['numbers'], axis=1)  # \u5c06\u5e8f\u53f7\u5217\u53bb\u6389", "execution_count": 120, "outputs": []}, {"metadata": {}, "cell_type": "markdown", "source": "## 1.2\u6570\u503c\u5c5e\u6027\u6982\u62ec\n\ndescribe()\u65b9\u6cd5\u53ef\u4ee5\u5c55\u793a\u6570\u503c\u5c5e\u6027\u7684\u6982\u62ec\u3002\n\u5176\u4e2d,count\u662f\u975e\u7a7a\u503c\u7684\u603b\u6570\u91cf\uff0cmean\u662f\u5e73\u5747\u6570\uff0cstd\u662f\u6807\u51c6\u5dee\uff0c25%\uff0c50%\uff0c75%\u8868\u793a\u5bf9\u5440\u7684\u5206\u4f4d\u6570\u3002"}, {"metadata": {"scrolled": true, "trusted": true}, "cell_type": "code", "source": "data.describe()    ", "execution_count": 121, "outputs": [{"output_type": "execute_result", "execution_count": 121, "data": {"text/plain": "       volatile acidity  fixed acidity  citric acid  residual sugar  \\\ncount       1599.000000    1598.000000  1596.000000     1595.000000   \nmean           0.527821       8.319962     0.270652        2.539969   \nstd            0.179060       1.741593     0.194488        1.411485   \nmin            0.120000       4.600000     0.000000        0.900000   \n25%            0.390000       7.100000     0.090000        1.900000   \n50%            0.520000       7.900000     0.260000        2.200000   \n75%            0.640000       9.200000     0.420000        2.600000   \nmax            1.580000      15.900000     1.000000       15.500000   \n\n         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\ncount  1594.000000          1594.000000           1594.000000  1595.000000   \nmean      0.087454            15.892723             46.470514     0.996748   \nstd       0.047126            10.467678             32.918860     0.001888   \nmin       0.012000             1.000000              6.000000     0.990070   \n25%       0.070000             7.000000             22.000000     0.995600   \n50%       0.079000            14.000000             38.000000     0.996750   \n75%       0.090000            21.000000             62.000000     0.997845   \nmax       0.611000            72.000000            289.000000     1.003690   \n\n                pH    sulphates       alcoho      quality  \ncount  1594.000000  1597.000000  1598.000000  1599.000000  \nmean      3.311098     0.657802    10.423373     5.636023  \nstd       0.154590     0.168888     1.065887     0.807569  \nmin       2.740000     0.330000     8.400000     3.000000  \n25%       3.210000     0.550000     9.500000     5.000000  \n50%       3.310000     0.620000    10.200000     6.000000  \n75%       3.400000     0.730000    11.100000     6.000000  \nmax       4.010000     2.000000    14.900000     8.000000  ", "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>volatile acidity</th>\n      <th>fixed acidity</th>\n      <th>citric acid</th>\n      <th>residual sugar</th>\n      <th>chlorides</th>\n      <th>free sulfur dioxide</th>\n      <th>total sulfur dioxide</th>\n      <th>density</th>\n      <th>pH</th>\n      <th>sulphates</th>\n      <th>alcoho</th>\n      <th>quality</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1599.000000</td>\n      <td>1598.000000</td>\n      <td>1596.000000</td>\n      <td>1595.000000</td>\n      <td>1594.000000</td>\n      <td>1594.000000</td>\n      <td>1594.000000</td>\n      <td>1595.000000</td>\n      <td>1594.000000</td>\n      <td>1597.000000</td>\n      <td>1598.000000</td>\n      <td>1599.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.527821</td>\n      <td>8.319962</td>\n      <td>0.270652</td>\n      <td>2.539969</td>\n      <td>0.087454</td>\n      <td>15.892723</td>\n      <td>46.470514</td>\n      <td>0.996748</td>\n      <td>3.311098</td>\n      <td>0.657802</td>\n      <td>10.423373</td>\n      <td>5.636023</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.179060</td>\n      <td>1.741593</td>\n      <td>0.194488</td>\n      <td>1.411485</td>\n      <td>0.047126</td>\n      <td>10.467678</td>\n      <td>32.918860</td>\n      <td>0.001888</td>\n      <td>0.154590</td>\n      <td>0.168888</td>\n      <td>1.065887</td>\n      <td>0.807569</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.120000</td>\n      <td>4.600000</td>\n      <td>0.000000</td>\n      <td>0.900000</td>\n      <td>0.012000</td>\n      <td>1.000000</td>\n      <td>6.000000</td>\n      <td>0.990070</td>\n      <td>2.740000</td>\n      <td>0.330000</td>\n      <td>8.400000</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.390000</td>\n      <td>7.100000</td>\n      <td>0.090000</td>\n      <td>1.900000</td>\n      <td>0.070000</td>\n      <td>7.000000</td>\n      <td>22.000000</td>\n      <td>0.995600</td>\n      <td>3.210000</td>\n      <td>0.550000</td>\n      <td>9.500000</td>\n      <td>5.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.520000</td>\n      <td>7.900000</td>\n      <td>0.260000</td>\n      <td>2.200000</td>\n      <td>0.079000</td>\n      <td>14.000000</td>\n      <td>38.000000</td>\n      <td>0.996750</td>\n      <td>3.310000</td>\n      <td>0.620000</td>\n      <td>10.200000</td>\n      <td>6.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.640000</td>\n      <td>9.200000</td>\n      <td>0.420000</td>\n      <td>2.600000</td>\n      <td>0.090000</td>\n      <td>21.000000</td>\n      <td>62.000000</td>\n      <td>0.997845</td>\n      <td>3.400000</td>\n      <td>0.730000</td>\n      <td>11.100000</td>\n      <td>6.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.580000</td>\n      <td>15.900000</td>\n      <td>1.000000</td>\n      <td>15.500000</td>\n      <td>0.611000</td>\n      <td>72.000000</td>\n      <td>289.000000</td>\n      <td>1.003690</td>\n      <td>4.010000</td>\n      <td>2.000000</td>\n      <td>14.900000</td>\n      <td>8.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "markdown", "source": "## 1.3\u6570\u636e\u7684\u63cf\u8ff0\uff08\u603b\u884c\u6570\u3001\u6bcf\u4e2a\u5c5e\u6027\u7684\u7c7b\u578b\u3001\u975e\u7a7a\u503c\u6570\u91cf\uff09\ninfo()\u65b9\u6cd5\u53ef\u4ee5\u5feb\u901f\u67e5\u770b\u6570\u636e\u7684\u63cf\u8ff0\uff0c\u7279\u522b\u662f\u603b\u884c\u6570\u3001\u6bcf\u4e2a\u5c5e\u6027\u7684\u7c7b\u578b\u548c\u975e\u7a7a\u503c\u7684\u6570\u91cf\u3002\n\u6211\u4eec\u4ece\u4e0b\u9762\u53ef\u4ee5\u770b\u5230\uff0c\u6570\u636e\u91cc\u90fd\u662f\u6570\u503c\u7c7b\u578b\u7684\u7279\u5f81\uff0c\u6ca1\u6709\u5b57\u7b26\u578b\u7279\u5f81\uff0c\u6240\u4ee5\u4e0d\u9700\u8981\u8fdb\u884c\u6570\u636e\u7c7b\u578b\u8f6c\u6362"}, {"metadata": {"scrolled": false, "trusted": true}, "cell_type": "code", "source": "data.info()", "execution_count": 122, "outputs": [{"output_type": "stream", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1599 entries, 0 to 1598\nData columns (total 12 columns):\nvolatile acidity        1599 non-null float64\nfixed acidity           1598 non-null float64\ncitric acid             1596 non-null float64\nresidual sugar          1595 non-null float64\nchlorides               1594 non-null float64\nfree sulfur dioxide     1594 non-null float64\ntotal sulfur dioxide    1594 non-null float64\ndensity                 1595 non-null float64\npH                      1594 non-null float64\nsulphates               1597 non-null float64\nalcoho                  1598 non-null float64\nquality                 1599 non-null int64\ndtypes: float64(11), int64(1)\nmemory usage: 150.0 KB\n", "name": "stdout"}]}, {"metadata": {}, "cell_type": "markdown", "source": "##  1.4\u7f3a\u5931\u503c\u7edf\u8ba1\u4e0e\u586b\u5145\n\u53ef\u4ee5\u770b\u5230\u4e0b\u9762\u6bcf\u4e2a\u7279\u5f81\u7f3a\u5931\u503c\u7684\u60c5\u51b5\uff0c\u6211\u4eec\u7528\u5217\u5747\u503c\u586b\u5145\u7684\u65b9\u6cd5\u586b\u5145\u8fd9\u4e9b\u7f3a\u5931\u503c"}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "data.isnull().sum(axis=0)\ndata_isnull = data.isnull().sum(axis=0)\ndata_isnull[data_isnull != 0].sort_values(ascending=False)", "execution_count": 123, "outputs": [{"output_type": "execute_result", "execution_count": 123, "data": {"text/plain": "pH                      5\ntotal sulfur dioxide    5\nfree sulfur dioxide     5\nchlorides               5\ndensity                 4\nresidual sugar          4\ncitric acid             3\nsulphates               2\nalcoho                  1\nfixed acidity           1\ndtype: int64"}, "metadata": {}}]}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "data.fillna(data.mean(), inplace=True)   #\u5747\u503c\u586b\u5145", "execution_count": 124, "outputs": []}, {"metadata": {}, "cell_type": "markdown", "source": "# 2.\u6570\u636e\u76f8\u5173\u6027\u5206\u6790"}, {"metadata": {}, "cell_type": "markdown", "source": "##  2.1\u7cfb\u6570\u77e9\u9635\u7ed8\u5236 \n\u76f8\u5173\u7cfb\u6570\u77e9\u9635\uff0c\u5e38\u7528\u7684\u6709\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570(Pearson product-moment correlation coefficient\uff0cPearson's r)\u7684\u65b9\u9635\uff0c\u901a\u8fc7\u5b83\u53ef\u4ee5\u6765\u8861\u91cf\u4e24\u4e2a\u7279\u5f81\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u7cfb\u3002\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570\u7684\u53d6\u503c\u5728[-1,1]\u8303\u56f4\u5185\uff0c\u5982\u679cr=1\uff0c\u8868\u793a\u4e24\u4e2a\u53d8\u91cf\u5448\u6b63\u76f8\u5173\uff0cr=0\u8868\u793a\u4e24\u4e2a\u53d8\u91cf\u6ca1\u6709\u5173\u7cfb\uff0cr=-1\u8868\u793a\u4e24\u4e2a\u53d8\u91cf\u5448\u8d1f\u76f8\u5173\u3002\u5176\u5b9e\uff0c\u76f8\u5173\u7cfb\u6570\u77e9\u9635\u5c31\u662f\u6807\u51c6\u5316\u7684\u534f\u65b9\u5dee\u77e9\u9635\u3002"}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "cols=['volatile acidity','fixed acidity','citric acid','residual sugar','chlorides','free sulfur dioxide','total sulfur dioxide','density','pH','sulphates','alcoho','quality']\n\ncm = np.corrcoef(data[cols].values.T)\n#\u8bbe\u7f6e\u5b57\u7684\u6bd4\u4f8b\nplt.figure(figsize=(10,10))\nsns.set(font_scale=1.0)\n#\u7ed8\u5236\u76f8\u5173\u7cfb\u6570\u56fe\nhm = sns.heatmap(cm,cbar=True,annot=True,square=True,fmt=\".2f\",\n                 annot_kws={\"size\":10},yticklabels=cols,xticklabels=cols)\nplt.show()\n", "execution_count": 125, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<matplotlib.figure.Figure at 0x7fc65ef06320>", "image/png": 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\n"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "markdown", "source": "## 2.2\u7ed8\u56fe\u8868\u793a\u5404\u4e2a\u7279\u5f81\u5206\u522b\u4e0e\u6807\u7b7e\u4e4b\u95f4\u7684\u5206\u5e03\u548c\u56de\u5f52\u5173\u7cfb"}, {"metadata": {"scrolled": true, "trusted": true}, "cell_type": "code", "source": "features = ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n       'pH', 'sulphates', 'alcoho']\nx = data[features]\ny = data['quality']\nsns.pairplot(data,x_vars=features,y_vars='quality',kind='reg',size=7,aspect=0.5)", "execution_count": 126, "outputs": [{"output_type": "execute_result", "execution_count": 126, "data": {"text/plain": "<seaborn.axisgrid.PairGrid at 0x7fc65f7f9518>"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "<matplotlib.figure.Figure at 0x7fc65e989a20>", "image/png": 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\n"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "markdown", "source": "## 2.3\u5206\u6790\n\u4ece\u4e0a\u9762\u5206\u5e03\u56fe\u548c\u7cfb\u6570\u77e9\u9635\u53ef\u4ee5\u770b\u5230\uff0c'residual sugar'\u8fd9\u4e00\u7279\u5f81\u4e0e\u6807\u7b7e\u7684\u76f8\u5173\u6027\u6700\u5dee\uff0c\u6211\u4eec\u540e\u7eed\u8bad\u7ec3\u6a21\u578b\u7684\u65f6\u5019\u53ef\u4ee5\u8003\u8651\u5254\u9664\u6b64\u7279\u5f81\uff0c\u5177\u4f53\u5206\u6790\u89c1\u540e\u7eed\u6b65\u9aa4"}, {"metadata": {}, "cell_type": "markdown", "source": "# 3.\u6a21\u578b\u8bad\u7ec3\u4e0e\u8bc4\u4f30\n\u6211\u4eec\u5c06\u6570\u636e\u96c6\u516d\u4e8c\u4e8c\u5206\uff0c\u5206\u4e3a\u8bad\u7ec3\u96c6\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0c\u5728\u8bad\u7ec3\u96c6\u4e0a\u8fdb\u884c\u8bad\u7ec3\uff0c\u5229\u7528\u9a8c\u8bc1\u96c6\u8fdb\u884c\u6a21\u578b\u8c03\u53c2\uff0c\u5e76\u5c06\u6700\u7ec8\u9009\u597d\u7684\u53c2\u6570\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8fdb\u884c\u6d4b\u8bd5\u3002\n\n\u6211\u4eec\u4ee5\u5e73\u5747\u7edd\u5bf9\u8bef\u5dee\u4f5c\u4e3a\u6a21\u578b\u7684\u8bc4\u4f30\u6307\u6807\uff0c\u5e76\u753b\u51fa\u5404\u4e2a\u6a21\u578b\u7684\u6b8b\u5dee\u56fe\u3002\n\u5e73\u5747\u7edd\u5bf9\u8bef\u5dee\uff08Mean Absolute Deviation\uff09\uff0c\u53c8\u53eb\u5e73\u5747\u7edd\u5bf9\u79bb\u5dee\uff0c\u662f\u6240\u6709\u5355\u4e2a\u89c2\u6d4b\u503c\u4e0e\u7b97\u672f\u5e73\u5747\u503c\u7684\u504f\u5dee\u7684\u7edd\u5bf9\u503c\u7684\u5e73\u5747\u3002"}, {"metadata": {}, "cell_type": "markdown", "source": "## 3.1\u6570\u636e\u96c6\u6807\u51c6\u5316\u3001\u5212\u5206\u3001\u5e76\u8f6c\u4e3adataframe\u683c\u5f0f"}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "data[features]=(data[features]-data[features].mean())/(data[features].std())\ntrain_data, test_val_data= train_test_split(data,  test_size=0.4,random_state=888)   \nval_data,test_data=train_test_split(test_val_data,  test_size=0.5,random_state=888) #\u6570\u636e\u96c6\u7684\u767e\u5206\u4e4b\u516d\u5341\u4e3a\u8bad\u7ec3\u96c6\uff0c\u767e\u5206\u4e4b\u4e8c\u5341\u4f4d\u9a8c\u8bc1\u96c6\uff0c\u767e\u5206\u4e4b\u4e8c\u5341\u6d4b\u8bd5\u96c6\ntrain_data_ = spark.createDataFrame(train_data)\nval_data_ = spark.createDataFrame(val_data)\ntest_data_ = spark.createDataFrame(test_data)", "execution_count": 127, "outputs": []}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "train_data.head(5)", "execution_count": 128, "outputs": [{"output_type": "execute_result", "execution_count": 128, "data": {"text/plain": "      volatile acidity  fixed acidity  citric acid  residual sugar  chlorides  \\\n761           0.710263       0.562901    -0.054819       -0.383033   0.181635   \n65            1.101194      -0.643269    -1.135591        1.496776  -0.030893   \n1526         -0.322912      -0.873016    -0.981195       -0.241161  -0.498453   \n1533         -0.769690      -0.183776     0.151042       -0.453970  -0.285926   \n209          -1.272316       1.539325     1.592072       -0.312097  -0.710981   \n\n      free sulfur dioxide  total sulfur dioxide   density        pH  \\\n761             -1.042237             -0.348995  0.335228 -0.395844   \n65              -1.137919             -1.079203 -0.290598  0.640773   \n1526             0.201628             -0.257718 -0.645939 -0.071901   \n1533             1.541176              1.141848  0.223852  0.511195   \n209             -0.850873             -0.835800  0.664051 -0.007113   \n\n      sulphates    alcoho  quality  \n761   -1.408925 -0.772719        5  \n65    -1.586668  0.447305        5  \n1526  -0.046226 -0.772719        6  \n1533  -0.283217 -1.335806        5  \n209    1.316473  0.071913        7  ", "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>volatile acidity</th>\n      <th>fixed acidity</th>\n      <th>citric acid</th>\n      <th>residual sugar</th>\n      <th>chlorides</th>\n      <th>free sulfur dioxide</th>\n      <th>total sulfur dioxide</th>\n      <th>density</th>\n      <th>pH</th>\n      <th>sulphates</th>\n      <th>alcoho</th>\n      <th>quality</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>761</th>\n      <td>0.710263</td>\n      <td>0.562901</td>\n      <td>-0.054819</td>\n      <td>-0.383033</td>\n      <td>0.181635</td>\n      <td>-1.042237</td>\n      <td>-0.348995</td>\n      <td>0.335228</td>\n      <td>-0.395844</td>\n      <td>-1.408925</td>\n      <td>-0.772719</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>1.101194</td>\n      <td>-0.643269</td>\n      <td>-1.135591</td>\n      <td>1.496776</td>\n      <td>-0.030893</td>\n      <td>-1.137919</td>\n      <td>-1.079203</td>\n      <td>-0.290598</td>\n      <td>0.640773</td>\n      <td>-1.586668</td>\n      <td>0.447305</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1526</th>\n      <td>-0.322912</td>\n      <td>-0.873016</td>\n      <td>-0.981195</td>\n      <td>-0.241161</td>\n      <td>-0.498453</td>\n      <td>0.201628</td>\n      <td>-0.257718</td>\n      <td>-0.645939</td>\n      <td>-0.071901</td>\n      <td>-0.046226</td>\n      <td>-0.772719</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>1533</th>\n      <td>-0.769690</td>\n      <td>-0.183776</td>\n      <td>0.151042</td>\n      <td>-0.453970</td>\n      <td>-0.285926</td>\n      <td>1.541176</td>\n      <td>1.141848</td>\n      <td>0.223852</td>\n      <td>0.511195</td>\n      <td>-0.283217</td>\n      <td>-1.335806</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>209</th>\n      <td>-1.272316</td>\n      <td>1.539325</td>\n      <td>1.592072</td>\n      <td>-0.312097</td>\n      <td>-0.710981</td>\n      <td>-0.850873</td>\n      <td>-0.835800</td>\n      <td>0.664051</td>\n      <td>-0.007113</td>\n      <td>1.316473</td>\n      <td>0.071913</td>\n      <td>7</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "markdown", "source": "## 3.2 \u5229\u7528\u9a8c\u8bc1\u96c6\u8bef\u5dee\u8fdb\u884c\u6a21\u578b\u9009\u62e9"}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "def draw_residual_plots(regression_model,train_data,test_data,assembler):\n\n    train_output = assembler.transform(train_data)\n    train_features = train_output.select(\"features\",train_output.quality.cast('double')).toDF('features','label')\n\n    test_output = assembler.transform(test_data)\n    test_features = test_output.select(\"features\",test_output.quality.cast('double')).toDF('features','label')\n    \n    train_predict = regression_model.transform(train_features)\n    test_predict = regression_model.transform(test_features)\n    \n    train_data = train_data.toPandas()\n    test_data = test_data.toPandas()\n    train_predict = train_predict.toPandas()\n    test_predict= test_predict.toPandas()\n    train_y_pred = np.array(train_predict['prediction'])\n    test_y_pred = np.array(test_predict['prediction'])\n    y_train = np.array(train_data['quality'])\n    y_test = np.array(test_data['quality'])\n\n    plt.scatter(train_y_pred,train_y_pred-y_train,c=\"blue\",marker=\"o\",label=\"test data\")\n    plt.scatter(test_y_pred,test_y_pred-y_test,c=\"lightgreen\",marker=\"s\",label=\"train data\")\n    plt.legend(loc=\"upper left\")\n    plt.hlines(y=0,xmin=0,xmax=10,lw=2,color=\"red\")\n    plt.xlim([0,10])\n    plt.xlabel(\"predictive value\")\n    plt.ylabel(\"residual error\")\n    plt.show()\n    plt.close()\n\ndef calculate_metric_value(regression_model, regression_df,inputcols):\n    metric_dict={}\n    assembler = pyspark.ml.feature.VectorAssembler(inputCols=inputcols, outputCol='features')\n    output = assembler.transform(regression_df)\n    label_features = output.select(\"features\",output.quality.cast('double')).toDF('features','label')\n    dataset = regression_model.transform(label_features)\n    evaluator = RegressionEvaluator(predictionCol=\"prediction\")\n    metric_dict[\"mae\"] = (evaluator.evaluate(dataset, {evaluator.metricName: \"mae\"}))\n\n    return metric_dict", "execution_count": 129, "outputs": []}, {"metadata": {}, "cell_type": "markdown", "source": "### 3.2.1\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u8bad\u7ec3\u4e0e\u8bc4\u4f30"}, {"metadata": {"scrolled": false, "trusted": true}, "cell_type": "code", "source": "lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)\ninputcols=[\"fixed acidity\", \"volatile acidity\",\"citric acid\",'residual sugar',\n                                                            \"chlorides\",\"free sulfur dioxide\",\"total sulfur dioxide\",\"density\",\n                                                             \"pH\",\"sulphates\",'alcoho']\nassembler = pyspark.ml.feature.VectorAssembler(inputCols=inputcols, outputCol='features')\noutput = assembler.transform(train_data_)\nlabel_features = output.select(\"features\", output.quality.cast('double')).toDF('features','label')\n\nLRModel = lr.fit(label_features)\ndraw_residual_plots(LRModel,train_data_,val_data_,assembler)\nmetric_dict_test=calculate_metric_value(LRModel, val_data_,inputcols)\nmetric_dict_train=calculate_metric_value(LRModel, train_data_,inputcols)\nprint(\"train:\",metric_dict_train)\nprint(\"val:\",metric_dict_test)", "execution_count": 130, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<matplotlib.figure.Figure at 0x7fc65c46bf60>", "image/png": 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\n"}, "metadata": {}}, {"output_type": "stream", "text": "train: {'mae': 0.619197779010262}\nval: {'mae': 0.6453896009890899}\n", "name": "stdout"}]}, {"metadata": {}, "cell_type": "markdown", "source": "### 3.2.2 \u968f\u673a\u68ee\u6797\u56de\u5f52\u6a21\u578b\u8bad\u7ec3\u4e0e\u8bc4\u4f30"}, {"metadata": {"scrolled": true, "trusted": true}, "cell_type": "code", "source": "RFR = RandomForestRegressor()\n\ninputcols=[\"fixed acidity\", \"volatile acidity\",\"citric acid\",'residual sugar',\n                                                             \"chlorides\",\"free sulfur dioxide\",\"total sulfur dioxide\",\"density\",\n                                                             \"pH\",\"sulphates\",'alcoho']\nassembler = pyspark.ml.feature.VectorAssembler(inputCols=inputcols, outputCol='features')\noutput = assembler.transform(train_data_)\nlabel_features = output.select(\"features\", output.quality.cast('double')).toDF('features','label')\n\n\nRFRModel = RFR.fit(label_features)\n\ndraw_residual_plots(RFRModel,train_data_,val_data_,assembler)\nmetric_dict_test=calculate_metric_value(RFRModel, val_data_,inputcols)\nmetric_dict_train=calculate_metric_value(RFRModel, train_data_,inputcols)\nprint(\"train:\",metric_dict_train)\nprint(\"val:\",metric_dict_test)", "execution_count": 131, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<matplotlib.figure.Figure at 0x7fc65c455b70>", "image/png": 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\n"}, "metadata": {}}, {"output_type": "stream", "text": "train: {'mae': 0.43603584841231596}\nval: {'mae': 0.49778909960545314}\n", "name": "stdout"}]}, {"metadata": {}, "cell_type": "markdown", "source": "### 3.2.3gbt\u6a21\u578b\u8bad\u7ec3\u4e0e\u8bc4\u4f30\n\u4ece\u4e0b\u9762\u53ef\u4ee5\u770b\u5230\uff0c\u9a8c\u8bc1\u96c6\u8bef\u5dee\u548c\u8bad\u7ec3\u96c6\u8bef\u5dee\u76f8\u5dee\u8f83\u5927\uff0cgbt\u6a21\u578b\u53ef\u80fd\u5df2\u7ecf\u8fc7\u62df\u5408\u3002"}, {"metadata": {"scrolled": true, "trusted": true}, "cell_type": "code", "source": "gbt = GBTRegressor()\ninputcols=[\"fixed acidity\", \"volatile acidity\",\"citric acid\",'residual sugar',\n                                                             \"chlorides\",\"free sulfur dioxide\",\"total sulfur dioxide\",\"density\",\n                                                             \"pH\",\"sulphates\",'alcoho']\nassembler = pyspark.ml.feature.VectorAssembler(inputCols=inputcols, outputCol='features')\noutput = assembler.transform(train_data_)\nlabel_features = output.select(\"features\", output.quality.cast('double')).toDF('features','label')\n\n\nGBTModel = gbt.fit(label_features)\n\ndraw_residual_plots(GBTModel,train_data_,val_data_,assembler)\nmetric_dict_test=calculate_metric_value(GBTModel, val_data_,inputcols)\nmetric_dict_train=calculate_metric_value(GBTModel, train_data_,inputcols)\nprint(\"train:\",metric_dict_train)\nprint(\"val:\",metric_dict_test)", "execution_count": 132, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<matplotlib.figure.Figure at 0x7fc65c9fceb8>", "image/png": 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l1DVjDtOm1ebsWT1BjbN50s7gIKovCQ5CE5WdxVDE3Wm3c3Ohd28/fv/dQEgLXaXXrn8plqULfPHxUWc8W0FhNzudDgz3Kl0JpbFLdSizMsoAj/zNvnLuSIchvI8EB6GJys5iKOKutNtFw1lffmngwgX7lnwuXeBLAx91hk/yyOYHdpNuuOJU+R5KbxrTxOW0HBc459SSWRnrrxkkOAhNVHYWQ5F+/RS3DCnNmOHLsmWOZVb1cSzfXrmMGDnF7xzhvxgNzh32rEZgUFA4w0n2Gn5w+h6eIKc3akuCg/CYm5eq9uunFE88lxYQYObJJwtU21hWWmYmJCXVsnrcOoGehdjYAiZPLlBlKElB4SB7OWVwbtczFA4lqdFjcDQwrHgmlqTFvsUHFrnaFvak29b/8Z/0ULQjwUG4XdEwztatBs6dK1mqOn164Yf/1q0Gzp/XExZm5oEHFGbPNlKvnnvqMmWKL4pi/eFakkCvUNu2CvMS81R73fOkuhQYOirRNMf1nc9ZZDrcY7jntro0qqNeF86edNsSFLQnwUG43c2rkm5eqpqYaPTI5rfcXPj++8re8hZ0OrjjDhNffaVOYDBi5DQnOGT+1anyTY0taKdvRxD1VUmJcSEzG4IdKzNxomub6kTVVPUTsAivVtmqpK1bDeTmlpzF4O7AcOCAnrS0it/ytWvDgQPZ7NiRp8qZz0aM/Mx/OGD4iQJfx4NNkNKALvpoGhCsSmC4eBGef97xv5ga8y2i6pGeg3CrylYluXupKhTOMUyf7sv33xeuTPLxAZOp/GuHDTPStKk6r2vEyH72kGI441T5YOMtdNf3xBdfl+pRNL5/8SIMGeLHrffl2i5USg+lt6pzDPn5kHotG1RqZ+E+EhyEW1W2KsldS1Wh4t3XFbnzToU33lBnAryox+BsYGikhNFTH4MrRwYVfSBnk1V4JkRTmLbPsXuosTIKbppjCPjjf+H1JDgIt6psVZK7lqqC7d3XPj4WzGYICyucHJ89W508TgoKhzjgdGAIUOrRle4uBQYoPChoq2GDU2Vvz+/ArYZI6hHk8pLZS1y0eUiQ8E4SHITbFS1JLVqVFB5ekljPHa5ehS+/tP3W/uKLXDp2NKsWoPLJ5Ue+56LhnFPl6yh+xNCfOjhfIbOlMB1GKmedKt8mtwPtfO90OTiB82dOC+8gwUG4ncHgmVVJjux8Dg83qxoYjBj5Dzu5ZLjoVHk/xZ8+rgYGzJy6cYothi1O3yPKN1KVwOCINql9aBlWMtYkm9u8gwQH4TJ7z2EoWpXkLtOn+/KPf9i381nNIa18cvkP33HJkOZU+UZKGN3o6VJggD+GkvIcCwwfxg+ma/s6JCYWLldVc/L593M5EGm7TNP6AbKvwQtJcBBOq2hzmyfPYSiqx+TJvixfbntlj9q7rxUUfuYHpwNDMyWKe7nfpVVJRWP7J3JTwcHNg/e2rcPsSf6qfRCUmXy2IzAAqiwbFuqT4CCcZmtzm6eMGweffFJZj8FCWJiZHj1MzJlzQ7Xd1woKRznEOUOKw2V9cupyT+27aE4Ll5erXuAc3xu2OxwYAF5/XZ3046L6kU1wwimpqbB+vXWOIijZ3OZuilKYDmPJksqva9zYzHff5bJokXqBoXAoaQdHDP91qvzdtdtxG7e7FBiMGPmNYxy84fju6/NbOtHvRrwq4/tFZ0JcvnGd58bZ/6Vg5xsPMkBRpw5CfZp+adi9ezdz5szBbDYzdOhQxowZo2V1hB3y86F/fz+OHvXBbC7/Gk9sboPCnos9cwyxsQoNG6r72kc4xHlDqlNl71Du5lZaulyHs5xmv2GPU7/Fw/o0oIFK4/zFQ0kGGLLY/nKJ42vJXIMX06znYDKZmDVrFsuWLWPLli1s3ryZEydOaFUdYaf+/f04csSA2awDyj8gx52b24rYc1iQj4+FZ5+9oeqSWQWF86Ry7lrFWUUrc6dyN+242+UeQzL/j1/yjjpcNji9BT2VGJcPCiqiKDBvXvk9yPKsevkhkt/vT3djDE19PXvmtHCMzmKxuPe3uAK//PIL77//Ph9//DEAS/4YGxg7dmzFhXSVn9YlhBCiHE58zGvWc0hLSyMsrOSbQ2hoKGlpzq34EEIIoS7N5hzK67DobPUMLBYuX3auO1/dhIQEeqwtpk+vPBUFgF5vYf/+bNUS192svL0UFdXr2WdvMG+eekNJRoz8aj7I776OD+P45Tagg+/dNCHCpc1lCgqn+L1wjsFBgekRdKx3O40IU2WDWybXKz2P4WYfxg/CdD2AdevyaeQfqEqGWXt48nfE24U4UUaz4BAWFsbFiyW7SdPS0mjUqJFW1REVsDcVRdu2JrcEhsr2UhTNJWzbVpuUFIvb0nKc5bRTgQHgQd+e1KeB7QsrYcbMaU44FRgAWtdrSrgKaVCLNrgdS8mCW+0v98GbBtpF1gYcO5pVaMvmb31WVhaBgeovNWvXrh1nzpwhJSWF0NBQtmzZwjvvvKP66wjn2JeKwoJeXxgY1Doc52a29lLMnm3kb3+rzZEjOW5Jy6EokLTCh7ajHS/bSYl2eTWOgsJpTrDP8KNT5dspHWhOC5fqUORSbhY76q11KDAARNq5GU54l0qDg8ViYfjw4WzatEn9FzYYmDFjBqNHj8ZkMhEfH0+rVq1Ufx3hHFtZTQFCQsx8/XWuW4eSKjsoKDHRiL+/e9JymDFzPjOLvn39aDeggLZ2ljOk30JoPT+a05ImRKhy3rMzgaHutTC6B3Rx+QS5oj0Ml7JzmDBd4cn3bZf5MH4wBRl12bYtH1A/V5K96VqEayoNDjqdjoiICK5fv05QkPrrkXv06EGPHj1Uv69wjT3LRAGGDFHcFhhA24OCsslid/BaEvc6Vq5bvQ6qDOEoKJzhpMPnPReJCgingaPngd7EjJnzpLLb8C3Ux67AAJB+NoCfv/ElQOVhJG9J11JT2GxSf39/hgwZQvfu3fEvFaYnTZrk1ooJ7VT2oQwWGjc2ExvrvpTbRbQ4KEhB4aL5Ih+tzKf1KMfKtlM60AjX1+4X9RicCQyBpvo0szTnNtq4XI9ssgoDg4O2b88jwN+1lCDl8ZZ0LTWFzeAQGRlJpAwa1iiVfSg3bmxmx45c1Xccl0eLg4IucZHdvtucCgy34/o5CAoKp8wn2e/rXI/hkXp90V93rWGKhpLO5l5yKF/Th/EDWbe8Fg391Z+jtHeIUajH5jv55Zdf9kQ9hBep7ENZ7VQUpVM8l2fGzMIPGk8dFHTjBg6v4btNaatKYDBi5GDBQU75ObcyqpMSTUitEK66cPKaESPHOcphw0GHE/mt+aQWIbXdkw5D67PIayKb7+a8vDw++OADfvzxR3Q6HV27duWFF17Az8/PE/UTGvHU6W1lUjyXYwDxzJ6td/tBQUUyMnRQ1/Z1W2Z3YfqffKhVC5pxq8uBwYyZ4xx1OjB0VKKJohV6nWvHeh7iAL8ZjjlV3h3/LkWTz/XqaXMWeU1m8x3917/+FZPJRGJiIgBffPEFs2bNYu7cuW6vnNCOp05vs5e7DwqCwg8io52x791XmxBSy/VvyUVDOKeM/yPZ/6BT9+iuPEw4TVVZGeVMYLg/N4Zg30BVVyUpSuEmx61bDVy8qKdpUzNBQRZSysmO7s6zyGsym8Hh8OHDfPnll8U/d+jQgYEDB7q1UsJ7eOJDWWulV8EEtKrD2C9sl6mtwkIcBYXfOMZ/Dfuc2o56p7EDEfpmqixXvch5p1dGBfsGqppdNTMT+vb148SJkkZJSfEhJQXuvFPh+nWdR4YYazq73pK5ubnFK5Xy8tyz2UkIrZReBdO6ZeUpXDoZuxKmD1PlOM3TnCgMDE64V+lKlL6VKqkosslip2GbU2XvVO5RrcegKDBjhi9JSb4oSvn/Dtev69i2LZfMTO17s9WdzeAQGxvL448/ziOPPIJOp2PLli0MGjTIE3UTwu3s3dNRJEBf1+VvyUX7B5zd9Xyv0pXmtFAtR5Ezk/CtjG0J0gepWo+ZM31ZtqzyLtm5c3oyM2Xy2RNsviXGjBlDmzZt2LNnDxaLhYkTJ9K9e3dP1E0It7t5Fczv3zfhw/jBAOh1Fv72bj5hYSUfRK7uYygawnFm/wD80WPA9R5D0SqxjAx46oUCXrQ/jx4ArfW3qz6UtGqV7XMhQkNl8tlTKg0OJpOJ119/ndmzZ0tAENXSzXs6zIqB498V7uuJiDDRpl4uao1cFAUGZ4dwblPauvxNvSgoFA8lheBQYIg0tuBOfXvVU2JMn+5Ldrbtv5dMPntOpcHBx8eHs2fPeqouogYKIJABSnylz7uTpzbamTFzgXPsMnzjVPl2Sgda09blJbO2lg7b0lbfjiDqu1SH0lJTYdcuH3butCfzryI7oT3I5r9Ily5dmDVrFoMHDy6TPqNlS9fPwBVCj17zc4TdvafjBvkc4iC/G5KdKq/WUBI4N78AcHt+B5obmqkWGLKzoVMnf9LT7fs7tW2r8O23eZJDyYNsHhPaq1cv60I6Hdu3b3dbpSojh3cUkoNMSqjVFu7I9mnGzEH28pvBuc1tjZRQetLH7h5DRW1RNJz0wkQjA979spySFeupxBBGuKqH9LRp4096unVvzZqFp54y8uabjifXk9+REiEhjvfAbTb3hg0bqFfPwX30QlRBau/pKJpjcDYwhCtNuY/7XT5B7hIXySWHvYYfGPCufeWK8iTVrl04tKdmYEhNxe4ew6hRRubPl6EkLdg8z+Gpp55yy3kOQlRnrk4+91B605gmLk8+O3u06Iq/uy9P0p49lfUYLOh00LSpbHDTmqbnOQhRXTm7saztta7cGhBKIPVc/raeRabTR4s2cO1k00pFR5sqfX7Dhhzat5cNblqT8xyEUFHppaKOuievK60D1Jl4Bsgh2+EyPZUYAlA3T9LNmjaF4GBzuXMOwcFmoqNlH4M3kPMchFCRK0tFw2uFur65zVKYyA/gmjHH7pVJH8YP5J+LaxF2izrzC7ZSse/bb6Zzp4Aycw/BwWb27891+bWFOuQ8ByFUYMTIWU6Th3O5xzor9xPo6AEK5bhuvl4SnByYx163XN05Bpup2APiSU7Wk5paOAcRHW1y65GzwnE2vyJcvXqViRMnMnz4cACSk5P57LPP3F4xIRyRmwunT+vI1eiL51lOs9fwQ+EhOQ4IN0bQTenFrbR0efL5Otc4X3DeqfJqZJl1RtOmMHSoBAZvZPPdOH36dDp27EhmZiYAUVFRrFq1yu0VE8IeigLjx8MDD/gTHV2XBx7wZ/p0XxRF65rZ1lOJobv+IZrRXJWdz1sM6/g2z7GcTRHZbRmgxLt9J7qoemy+I9PS0hg2bBirV68GwNfXF71evTXPomqxNZas9pp4WwrTbQMUTm568tD50m2RWZDn0DBOd2MMYXrXN5a5MgEOcGudcM13qAvvZPPtbLhpW2JmZiY2NlXbtHXrVt5//31OnjzJmjVraNeunUv3E55jcyxZiffYh41Wh84Xnd52hUslB+TYGRiic2JoWDuQAL3rQVRB4QwnnT6kp7NyP2GEu1QHUX3ZfEvHxMQwY8YMcnJyWLduHatWrSI+vuJEafa47bbbWLRoEa+//rpL9xE1m1aHzmdyna8M650q27C2eqemXeKiw4GhKDiB53t5omqxGRxGjx7Npk2byMzMZNeuXYwYMcLlw35atGjhUnkhwDrddmnuOnTejJkrXHK4XDulA5HcqurYvtnseBk1g5Oo3uzqDA8cONBrzo12JoFUdaVFWxhMCmRW/HxwcF0a+HiuXnFxsHBheY/7EBmpfj0yTBnszXR8GCc0MJgWdVxfkqOYFc4p5wBYvyGLhr3tLzu47mAiaoWj17m/t9DQUpenzU9X+HyQPsgj9ZDPC+e5LQHuyJEjuXLlitXj48eP5+GHH3b6vpJlsZBWGSczqXxjVXp6DoqKbytbE+CvTQ4Egli3zlQm3fbkyUYuX1atGsVs/f1La5PXgXq1/AConxXG5SzX/73Ok1qclsORwACgXPfhKjku18F+FTeUJ+ohWVlLuCUrq7OSkpLcdWtRQ9iTvG4A8bz7bhCvvpqrerptV7WsdasqQzilA2Sugx+q9ypd8acu/tSV5arCIXJ0hvBajiSvUzvdNpQ3r4kdAAATqklEQVTfa3F2yagrXEnJ0YgwmWMQTqkwOOTlVZ4GwM/Pz+kX/eabb/jrX/9Keno6Y8eO5fbbb+fjjz92+n7Cc7Q+1tNTXE25XZTATkv3Kl1VqYO37W0RnlFhcLjnnnvQ6XRl9jQU/azT6Th27JjTL9q7d29693ZwwFR4BW841tMTnE25DYVnMah9cpq9rn4TzZAegej1hb0GNergTXtbhOdUGBySk50771aIqszVHcdqHKlZ+pt6fj6kXssGOxc6PTc4FP/rwU6/thBFZM5BiFIcGd8vb+hIjSGWMnUI+ON/DysdoLSYZxHasxkckpOTef3110lOTsZoLMlV48qwkhDVQQDesaGsaEUSQBNDEzKcTBtemiuT4KJ6sPkVZ+bMmYwfP57IyEh27drFmDFjePXVVz1RNyEq5Q2TvmpTFHjrrVoOlWlEGOE0JZymGPQyGCDUYfOdZDQaiY6OxmKx0KhRI1599VVGjBjBmDFjPFE/UYPZszKquq2SmTbNl827FaZNrfiaNql9aBlWMtZU3QKk8A42g4OPT2HemqCgIJKTkwkNDeXcuXNur5gQ3rwySu1ei6LA9Om+LF/uS3Dzyk8salo/wGvbRVQfNoND//79ycjIYMyYMQwbNgyz2UxCQoIn6iaE1ykKCmr0WkpP+r77fi2+3KUQ3DyX4GaVJK8C6tRx6WVVUTo4Ss+lerIZHEaNGgVA9+7d2bt3Lzdu3CAgQIPlE0J4ATUnoUtP+racCNMmqnJbj/CWyXjhPjaDw65du8p9vEePHqpXRgitedMO8A/jB5N+th4AcXFGXnutwON1EDWXzeCwbNmy4j8bjUaOHTtG27ZtJTiIasmb5jnSz9Yj/Ux9nn7ayOuvunrKtGO8KUgKbdh8v3366adlfj5x4gSffPKJ2yokhCjxzDNG5s1z71nY5fGmICm04fCXkZYtW3L8+HF31EUIUcrjjxtJfNlzZ2NUx6XBwnkOzTmYzWYOHz6M2ZnzCYUQDhk/vkDVoSRJoCcc4dCcg8FgICIigoXlncsohBCi2nB4zkEIoQ5PTfq6mmlW1EwVBoeVK1dWWnD48OGqV0ZUbTKm7RhPTfpKEj3hjAqDw5EjRwDIyMhg7969REdHA7Bnzx66desmwUFYkTFtIaqPCoPD3LlzAXjppZfYuHEjERERAKSkpLBgwQLP1E54Pcn7L0T1ZHPO4dy5c8WBASAiIoLTp0+7tVKi6pAhCyGqJ5vBoX79+ixevJihQ4cCsHbtWurXr+/2igkhPKN0MkEhiticHZw/fz7Hjx8nNjaW2NhYkpOTmT9/vifqJoTwgKIkerJYQJRms+cQGhrKe++954m6CCGE8BIVBocDBw7QsWNHycoqRBUnSfSEMyoMDuvXr6djx45ldkgX0el0LgWH+fPn891331GrVi2aNWvG3LlzqVevntP3E95LDoXRniTRE87QWSwWi6df9D//+Q9dunTBYDDw1ltvAfDaa6/ZVfbyZVkuCRASEugVbZHJdc33NnhLW3gDaYsS0hYlQkIc/2Jmc85h3759tG3blrp167JmzRoOHz7M888/X2Z5q6O6detW/Oe7776bf//7307fS2hLhiyEqJ5s9hxiY2PZtGkTJ06cYNy4cQwcOJAff/yR5cuXq1KBF154gX79+jFo0CBV7ieEEMJ1NnsOBoMBnU7H7t27GTZsGCNGjLDrm/7IkSO5cuWK1ePjx4/n4YcfBuDDDz/Ex8eHgQMH2l1h6SYWki5zCWmLEtIWJaQtSrhlWElRFA4cOMDXX3/NnDlzADCZTDZvnJSUVOnz69evZ+fOnSQlJaHT6eyrrRBCCI+wGRzGjRvHrFmz6NKlC61ateL06dNERka69KK7d+/mo48+YsWKFfj5+bl0LyGEEOrTZLVS7969MRqNxWk42rdvz6xZs+wqK93EQtJlLiFtUULaooS0RQm3DCtdvXqVuXPncuHCBVauXElycjK//PILw4YNc6qSAN98843TZYUQQrifzWQq06dPp2PHjmRmZgIQFRXFqlWr3F4xIYQQ2rEZHNLS0hg2bBg+Pj4A+Pr6otdLgi4hhKjObH7KGwxlR54yMzPRYJpCCCGEB9mcc4iJiWHGjBnk5OSwbt06Vq1aRXx8xTtihRBCVH02g8Po0aPZtGkTmZmZ7Nq1ixEjRshuZiGEqOYqDQ4mk4nFixeTkJDg0C5mIYQQVVulcw4+Pj7s27fPU3URQgjhJWxOSPfs2ZOPP/6Yq1evkpeXV/y/EEKI6svmDuk2bdqUXKzTYbFY0Ol0HDt2zO2VK4/seCwkuz9LSFuUkLYoIW1Rwi07pJOTk52qjBBCiKpLdrMJIYSwIsFBCCGEFQkOQgghrEhwEEIIYUWCgxBCCCsSHIQQQliR4CCEEMKKBAchhBBWJDgIIYSwIsFBCCGEFQkOQgghrEhwEEIIYcVm4j13ePfdd9m+fTt6vZ6GDRsyd+5cQkNDtaiKEEKIcmjScxg9ejRffvklGzdupGfPnixevFiLagghhKiAJsEhICCg+M95eXnodDotqiGEEKICNg/7cZe//e1vbNiwgcDAQJYvX05wcLAW1RBCCFEOtwWHkSNHcuXKFavHx48fz8MPP1z885IlS7hx4wYJCQl23VdOdiokp1yVkLYoIW1RQtqihFtOgnNWUlKSXdcNGDCAsWPH2h0chBBCuJ8mcw5nzpwp/vOOHTuIiorSohpCCCEqoMlS1nfeeYfTp0+j0+lo0qQJf/nLX7SohhBCiApoEhwWLVqkxcsKIYSwk+yQFkIIYUWCgxBCCCsSHIQQQliR4CCEEMKKBAchhBBWJDgIIYSwIsFBCCGEFQkOQgghrEhwEEIIYUWCgxBCCCsSHIQQQliR4CCEEMKKBAchhBBWJDgIIYSwIsFBCCGEFQkOQgghrEhwEEIIYUWCgxBCCCsSHIQQQliR4CCEEMKKBAchhBBWJDgIIYSwIsFBCCGEFU2Dw8cff0zr1q1JT0/XshpCCCFuollwuHDhAj/++CPh4eFaVUEIIUQFNAsOc+fO5bXXXkOn02lVBSGEEBUwaPGi27dvp1GjRrRp08bhsiEhgW6oUdUkbVFC2qKEtEUJaQvnuS04jBw5kitXrlg9Pn78eJYsWcI//vEPp+57+XKWq1WrFkJCAqUt/iBtUULaooS0RQlngqTbgkNSUlK5jx8/fpzU1FQGDRoEwMWLF4mLi2PNmjWEhIS4qzpCCCEc4PFhpdatW7Nnz57in3v16sUXX3xBcHCwp6sihBCiArLPQQghhBVNJqRL27Fjh9ZVEEIIcRPpOQghhLCiec9BaMeMmWwqXs0RQCB6+f4gRI0kwaEGyyaLzYa1FT4/QImnHkEerJEQwlvI10IhhBBWJDgIIYSwIsFBCCGEFQkOQgghrEhwEEIIYUWCgxBCCCuylLUGCyCQAUp8pc8LIWomCQ41mB697GMQQpRLhpWEEEJYkeAghBDCigQHIYQQViQ4CCGEsCLBQQghhBUJDkIIIazoLBaLRetKCCGE8C7ScxBCCGFFgoMQQggrEhyEEEJYkeAghBDCigQHIYQQViQ4CCGEsCLBQQghhJUqERx2795Nnz596N27N0uXLtW6Opq5cOECI0aMoF+/fjzyyCP885//1LpKmjOZTAwePJixY8dqXRVNZWZmkpCQQN++fenXrx+//PKL1lXSTFJSEo888ggDBgxgwoQJ3LhxQ+sqedTUqVOJjo5mwIABxY9du3aNUaNGERMTw6hRo7h+/brN+3h9cDCZTMyaNYtly5axZcsWNm/ezIkTJ7SuliZ8fHyYMmUKW7duZfXq1axatarGtkWR5cuX06JFC62robk5c+bwwAMP8O9//5uNGzfW2DZJS0tj+fLlrF27ls2bN2MymdiyZYvW1fKouLg4li1bVuaxpUuXEh0dzbZt24iOjrbrS7bXB4dDhw4RGRlJREQEvr6+PPLII2zfvl3rammiUaNG3HHHHQAEBAQQFRVFWlqaxrXSzsWLF9m5cyePPvqo1lXRVHZ2Nvv27StuB19fX+rVq6dxrbRjMpnIz89HURTy8/Np1KiR1lXyqM6dOxMUVPYQr+3btzN48GAABg8ezLfffmvzPl4fHNLS0ggLCyv+OTQ0tEZ/IBZJTU3l2LFjtG/fXuuqaOaNN97gtddeQ6/3+rexW6WkpBAcHMzUqVMZPHgw06ZNIzc3V+tqaSI0NJRnn32WBx98kG7duhEQEEC3bt20rpbmrl69WhwkGzVqRHp6us0yXv9bVV7qJ51Op0FNvEdOTg4JCQkkJiYSEBCgdXU08d133xEcHMydd96pdVU0pygKR48eZdiwYWzYsAE/P78aOzd3/fp1tm/fzvbt2/n+++/Jy8tj48aNWlerSvL64BAWFsbFixeLf05LS6tx3cTSCgoKSEhIIDY2lpiYGK2ro5mDBw+yY8cOevXqxYQJE/jpp5+YOHGi1tXSRFhYGGFhYcW9yL59+3L06FGNa6WNH3/8kaZNmxIcHEytWrWIiYmp0ZPzRRo2bMilS5cAuHTpEsHBwTbLeH1waNeuHWfOnCElJQWj0ciWLVvo1auX1tXShMViYdq0aURFRTFq1Citq6OpP/3pT+zevZsdO3awYMECunTpwttvv611tTQREhJCWFgYp06dAmDPnj01dkI6PDycX3/9lby8PCwWS41ui9J69erFhg0bANiwYQMPPfSQzTIGd1fKVQaDgRkzZjB69GhMJhPx8fG0atVK62pp4sCBA2zcuJHbbruNQYMGATBhwgR69Oihcc2E1v785z8zceJECgoKiIiIYO7cuVpXSRPt27enT58+DBkyBIPBwO23387jjz+udbU8asKECezdu5eMjAy6d+/OK6+8wpgxYxg/fjxffPEFjRs3ZuHChTbvI+c5CCGEsOL1w0pCCCE8T4KDEEIIKxIchBBCWJHgIIQQwooEByGEEFYkOIgaZ9GiRcyfPx+Azz77jKSkpEqvT01NZfXq1WUee/755zl79qy7qmiXKVOmsGLFCk3rIKovr9/nIIQtiqJgMDj3Vh42bJjNa86dO8fq1avLrJf/6KOPnHo9IaoK6TkIr9W6dWsWLVrEE088QZ8+ffj666/LPLds2TJGjBjB+++/DxR+YD/66KMMGTKEF154gcuXLwOQlZVFQkIC/fv357nnnivzjb90LwJgyZIlxMbGMnDgQJ544gnMZjOzZs3i5MmTDBo0iISEBKBwx+lvv/3G/v37i7NdFomLi2Pv3r0ArF+/nqFDhxIXF8fTTz9dvIu5tA0bNvDSSy8V/6woCt26dSM1NZXjx4/z5JNPMmTIEPr3719hL+fmXkTpn7Ozs5k2bRqPPvoosbGxzJ49G5PJZPsfQNRo0nMQXk2n0/H5559z6tQphg0bRqdOnWjYsCEAZrOZTz/9FICNGzdy9uxZ/vWvf6HX61m1ahXz5s3jnXfeYfHixdStW5evvvqK9PR04uLi6Nevn9VrrV+/nh07dvDZZ58REBBARkYGer2eGTNmMH/+fNatW2dVplOnTuTm5pKcnEybNm347bffyMzMpHPnzuzfv5+tW7eycuVKfH192bVrF4mJiXz++edl7tGnTx/mzp1Leno6wcHB7N69m6ioKJo2bUp2djZJSUn4+vqSk5PD0KFDeeCBBxxKCTF37lw6d+7MnDlzMJvNTJw4kbVr1/LYY4858k8hahgJDsKrDR06FICoqCjatm3Lf//73+K8MEOGDCm+bseOHRw5cqT4MZPJVJyx9ueff2b69OkABAcH07t373Jf67vvvmPYsGHF5Ro0aGBXHQcNGsT69euZOnUq69atY8iQIeh0Onbs2EFycnLx38FisZCZmWlV3s/Pj4ceeojNmzfz9NNPs379euLi4gDIz89n5syZHD9+HJ1Ox6VLl0hOTnYoOOzYsYNDhw7xySefFN8zNDTU7vKiZpLgIKoMi8VSJl27v79/medefPHFcg/+cXeGmCFDhvDYY48xYcIENm/eXDx5bbFYiI+PZ9y4cTbvERcXxxtvvEFsbCx79+7lzTffBGDBggWEhIQwb948DAYDzz77bLnHXvr4+GA2m4t/Ln2NxWLhgw8+ICIiwtW/qqhBZM5BeLW1a9cCcObMmUoPN+rVqxerVq0qPhvXaDSSnJwMQHR0dPGQUEZGRoWnYD344IN89tlnZGdnF18LhafuFT1WnvDwcFq0aMHs2bNp2bIlTZo0Ka7Txo0bi1POm0wmjhw5Uu49OnXqRHZ2NgsWLODhhx/Gz88PKJwvCQsLw2AwFM9xlKdZs2YcPnwYKEzJ/PPPP5dpm6VLlxbPM6Snp5OSklLh30cIkJ6D8HK+vr488cQTZGRkMGvWrOL5hpsNHjyYa9eu8dRTTwGF35aHDRtGmzZt+L//+z8SExPp378/TZo0oWvXrhXeIy0tjccffxwfHx/q1q3LypUrad26NbfeeisDBgwgKiqK9957z6psXFwckyZNKv7GD4XHNY4fP54XX3wRk8lEQUEBffv2rfCAosGDB7Nw4UJWrlxZ/NiLL77IpEmT2LRpE82aNaNz587lln3sscdISEhg4MCBNG/enLvuuqv4ucTERN566y0GDRqETqejVq1aJCYmSk9CVEqysgqv1bp1aw4ePEjdunW1rooQNY4MKwkhhLAiPQchhBBWpOcghBDCigQHIYQQViQ4CCGEsCLBQQghhBUJDkIIIaz8f/Z5kOfg3YxAAAAAAElFTkSuQmCC\n"}, "metadata": {}}, {"output_type": "stream", "text": "train: {'mae': 0.2714584291042317}\nval: {'mae': 0.5124793408296222}\n", "name": "stdout"}]}, {"metadata": {}, "cell_type": "markdown", "source": "### 3.2.4 KNN\u56de\u5f52\u6a21\u578b\u8bad\u7ec3\u4e0e\u8bc4\u4f30\nKNN\u662fmoderlarts\u81ea\u7814\u7b97\u6cd5\uff0cknn\u56de\u5f52\u6a21\u578b\u8bfb\u53d6\u6570\u636e\u4ee5\u53ca\u8bad\u7ec3\u8fc7\u7a0b\u5982\u4e0b\u6240\u793a"}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "from modelarts_pyspark import KNNClassification,KNNRegression\nfrom modelarts_pyspark.common import MeasurementType, Role, DataType, DfUtils, Meta, Attributes\nfrom sklearn.metrics import mean_absolute_error\n\ndef get_schema(len_col,label_loc):\n    schema=[]\n    for i in range(1,len_col+1):\n        if i!=label_loc:\n            schema.append(dict(Meta('attr_'+str(i), MeasurementType.CONTINUOUS, DataType.INTEGER)))\n        else:\n            schema.append(dict(Meta('attr_'+str(i),  MeasurementType.CONTINUOUS, DataType.INTEGER, Role.TARGET)))\n    return schema\n\nschema=get_schema(12,12) #\u53c2\u6570\u5206\u522b\u8868\u793a\u7279\u5f81\uff08\u5305\u62ec\u6807\u7b7e\uff09\u7684\u5217\u6570\uff0c\u6807\u7b7e\u4f4d\u4e8e\u54ea\u4e00\u5217\uff0c\u6b64\u6570\u636e\u7279\u5f81\u52a0\u6807\u7b7e\u4e00\u517112\u5217\uff0c\u6807\u7b7e\u4f4d\u4e8e\u7b2c12\u5217\nknn_regression_train_df = DfUtils.build_data_frame(sqlContext, train_data_, schema)\nknn_regression_val_df=DfUtils.build_data_frame(sqlContext, val_data_, schema)\n\nknn_regression = KNNRegression().set_k(5)  # set_k()\u540e\u9762\u53ef\u4ee5\u8bbe\u7f6eKNN\u7684\u8d85\u53c2\u6570K\u503c\nKnnModel = knn_regression.fit(knn_regression_train_df)\n\nknn_predict_train = KnnModel.transform(knn_regression_train_df)  #\u5f97\u5230\u9884\u6d4b\u503c\nknn_predict_train=np.array(knn_predict_train.toPandas()) #\u5c06\u9884\u6d4b\u503c\u8f6c\u6362\u4e3aNumpy\u77e9\u9635\ntrain_data_label=np.array(train_data_.toPandas())[:,-1]\nmae_train=mean_absolute_error(train_data_label,knn_predict_train)  #\u5f97\u5230\u8bc4\u4f30\u7ed3\u679c\nprint(\"train mae:\",mae_train)\n\nknn_predict_val = KnnModel.transform(knn_regression_val_df)\nknn_predict_val=np.array(knn_predict_val.toPandas())\nval_data_label=np.array(val_data_.toPandas())[:,-1]\nmae_val=mean_absolute_error(val_data_label,knn_predict_val)\nprint(\"val mae:\",mae_val)", "execution_count": 133, "outputs": [{"output_type": "stream", "text": "train mae: 0.7565345846367744\nval mae: 0.8146875000000001\n", "name": "stdout"}]}, {"metadata": {}, "cell_type": "markdown", "source": "### 3.2.4\u5206\u6790\n\u53ef\u4ee5\u770b\u5230\uff0c\u968f\u673a\u68ee\u6797\u7684baseline\u6a21\u578b\u9a8c\u8bc1\u96c6\u96c6\u8bef\u5dee\u6700\u5c0f\uff0c\u5bf9\u6570\u636e\u62df\u5408\u66f4\u597d\uff0c\u6240\u4ee5\u6211\u4eec\u9009\u5b9a\u968f\u673a\u68ee\u6797\u6a21\u578b\uff0c\u5e76\u8fdb\u4e00\u6b65\u8fdb\u884c\u8c03\u4f18"}, {"metadata": {}, "cell_type": "markdown", "source": "## 3.3\u7279\u5f81\u7b5b\u9009\n\u4ece2.3\u7684\u5206\u6790\u53ef\u4ee5\u770b\u5230\uff0c'residual sugar'\u7279\u5f81\u4e0e\u6807\u7b7e\u76f8\u5173\u6027\u6700\u4f4e\uff0c\u6240\u4ee5\u6211\u4eec\u7528\u968f\u673a\u68ee\u6797\u6a21\u578b\uff0c\u89c2\u6d4b\u5254\u9664\u6b64\u7279\u5f81\u4e0e\u4e0d\u5254\u9664\u6b64\u7279\u5f81\u5728\u968f\u673a\u68ee\u6797baseline\u6a21\u578b\u4e0a\u7684\u8bef\u5dee\u53d8\u5316\u3002\u4e0d\u5254\u9664\u7279\u5f81\u4ece\u4e0a\u97623.2.2\u53ef\u4ee5\u770b\u5230\uff0c\u9a8c\u8bc1\u96c6mae\u8bef\u5dee\u5982\u4e0a\uff0c\u4e0b\u9762\u6211\u4eec\u5c06\u6b64\u7279\u5f81\u5254\u9664\u540e\uff0c\u770b\u770b\u8bef\u5dee\u53d8\u4e3a\u591a\u5c11\u3002"}, {"metadata": {}, "cell_type": "markdown", "source": "### 3.3.1\u5254\u9664\u65e0\u5173\u7279\u5f81\u540e\u518d\u6b21\u8bad\u7ec3\u6a21\u578b"}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "RFR = RandomForestRegressor(maxDepth =5)\n\ninputcols=[\"fixed acidity\", \"volatile acidity\",\"citric acid\",\n                                                             \"chlorides\",\"free sulfur dioxide\",\"total sulfur dioxide\",\"density\",\n                                                             \"pH\",\"sulphates\",'alcoho']\nassembler = pyspark.ml.feature.VectorAssembler(inputCols=inputcols, outputCol='features')\noutput = assembler.transform(train_data_)\nlabel_features = output.select(\"features\", output.quality.cast('double')).toDF('features','label')\n\n\nRFRModel = RFR.fit(label_features)\n\ndraw_residual_plots(RFRModel,train_data_,val_data_,assembler)\nmetric_dict_test=calculate_metric_value(RFRModel, val_data_,inputcols)\nmetric_dict_train=calculate_metric_value(RFRModel, train_data_,inputcols)\nprint(\"train:\",metric_dict_train)\nprint(\"val:\",metric_dict_test)", "execution_count": 134, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<matplotlib.figure.Figure at 0x7fc65c441b38>", "image/png": 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\n"}, "metadata": {}}, {"output_type": "stream", "text": "train: {'mae': 0.4408604504480212}\nval: {'mae': 0.49789858495580414}\n", "name": "stdout"}]}, {"metadata": {}, "cell_type": "markdown", "source": "### 3.3.2\u5206\u6790\n\u53ef\u4ee5\u770b\u5230\uff0c\u5254\u9664'residual sugar'\u540e\uff0c\u9a8c\u8bc1\u96c6\u8bef\u5dee\u7565\u6709\u51cf\u5c0f\uff0c\u4e14\u672c\u8eab\u6b64\u7279\u5f81\u4e0e\u6807\u7b7e\u76f8\u5173\u6027\u5f88\u4f4e\uff0c\u6240\u4ee5\u6211\u4eec\u5254\u9664\u6b64\u7279\u5f81\uff0c\u4e4b\u540e\u7684\u8bad\u7ec3\u8fc7\u7a0b\u4e0d\u518d\u6d89\u53ca\u6b64\u7279\u5f81\u3002\u53e6\u5916\u7279\u5f81\u4e2d\u201cPh\u201d\u3001'fixed acidity'\u503c\u4e0e\u6807\u7b7e\u76f8\u5173\u6027\u4e5f\u4e0d\u5927\uff0c\u5982\u679c\u6709\u611f\u5174\u8da3\u7684\u8bfb\u8005\u53ef\u4ee5\u8bd5\u8bd5\u5254\u9664\u8fd9\u4e24\u4e2a\u6807\u7b7e\u4e4b\u540e\u5bf9\u6a21\u578b\u51c6\u786e\u5ea6\u6709\u4f55\u5f71\u54cd"}, {"metadata": {}, "cell_type": "markdown", "source": "## 3.4 \u5229\u7528\u9a8c\u8bc1\u96c6\u8bef\u5dee\u8fdb\u884c\u6a21\u578b\u8c03\u53c2\n\u5254\u9664\u65e0\u5173\u7279\u5f81\u540e\uff0c\u6211\u4eec\u5bf9\u968f\u673a\u68ee\u6797\u8fdb\u884c\u8fdb\u4e00\u6b65\u8c03\u53c2\u3002\n\u6211\u4eec\u5bf9\u7b97\u6cd5\u7684\u4e24\u4e2a\u53c2\u6570\u8fdb\u884c\u8c03\u53c2\uff1anumTrees\uff0cmaxDepth\u3002\n\nnumTrees\uff08\u51b3\u7b56\u6811\u7684\u4e2a\u6570\uff09\uff1a\u51b3\u7b56\u6811\u7684\u4e2a\u6570\u8d8a\u591a\uff0c\u7ed3\u679c\u7684\u65b9\u5dee\u8d8a\u5c0f\uff0c\u8d8a\u80fd\u5bf9\u6297\u8fc7\u62df\u5408\uff0c\u540c\u65f6\uff0c\u8bad\u7ec3\u7684\u65f6\u95f4\u4e5f\u968f\u7740\u4e2a\u6570\u589e\u52a0\u800c\u589e\u52a0\u3002 \n\nmaxDepth\uff1a\u662f\u6307\u68ee\u6797\u4e2d\u6bcf\u4e00\u68f5\u51b3\u7b56\u6811\u6700\u5927\u6df1\u5ea6\u3002\u66f4\u6df1\u7684\u4e00\u68f5\u6811\u610f\u5473\u6a21\u578b\u66f4\u503e\u5411\u4e8e\u8fc7\u62df\u5408\u3002\n\n\u7531\u4e8e\u968f\u673a\u68ee\u6797\u6709\u6548\u5bf9\u6297\u8fc7\u62df\u5408\uff0c\u6240\u4ee5\u4e00\u822c\u6811\u7684\u6df1\u5ea6\u53ef\u4ee5\u5f88\u5927\u3002\n"}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "def get_mae_rf(num_tree,num_adh,train_features,inputcols,test_data_):\n\n    gbt = RandomForestRegressor(numTrees =num_tree, maxDepth =num_adh)\n\n    gbtModel = gbt.fit(train_features)\n\n    metric_dict_test=calculate_metric_value(gbtModel, val_data_,inputcols)\n\n    return(metric_dict_test['mae'])\n\ninputcols=[\"fixed acidity\", \"volatile acidity\",\"citric acid\",\n                                                             \"chlorides\",\"free sulfur dioxide\",\"total sulfur dioxide\",\"density\",\n                                                             \"pH\",\"sulphates\",'alcoho']\nassembler = pyspark.ml.feature.VectorAssembler(inputCols=inputcols, outputCol='features')\noutput = assembler.transform(train_data_)\nlabel_features = output.select(\"features\", output.quality.cast('double')).toDF('features','label')\n", "execution_count": 135, "outputs": []}, {"metadata": {}, "cell_type": "markdown", "source": "### 3.4.1\u5bf9\u51b3\u7b56\u6811\u4e2a\u6570\u8c03\u53c2\n\u6211\u4eec\u5c06\u51b3\u7b56\u6811\u6df1\u5ea6\u5b9a\u4e3a\u51fd\u6570\u672c\u8eab\u9ed8\u8ba4\u503c20\uff0c\u4ece\uff082,50\uff09\u8303\u56f4\u5185\u641c\u7d22\u51b3\u7b56\u6811\u4e2a\u6570\uff0c\u5e76\u4ee5\u6b64\u503c\u8bad\u7ec3\u8bad\u7ec3\u96c6\uff0c\u7136\u540e\u5bf9\u9a8c\u8bc1\u96c6\u8fdb\u884c\u6d4b\u8bd5\uff0c\u627e\u5230\u4f7f\u9a8c\u8bc1\u96c6mae\u503c\u6700\u5c0f\u7684\u51b3\u7b56\u6811\u4e2a\u6570\u3002"}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "plot_mae_num_tree = {}\nfor num_tree  in range(2,50):\n    my_mae = get_mae_rf(num_tree,20,label_features,inputcols,val_data_)\n    plot_mae_num_tree[num_tree] = my_mae\n    \nplt.plot(list(plot_mae_num_tree.keys()),plot_mae_num_tree.values())\nplt.xlabel(\"Iter\")\nplt.ylabel(\"Mean Absolute Deviation \")\nplt.show()\nplot_mae_num_tree_sorted = sorted(plot_mae_num_tree.items(),key=itemgetter(1))\nprint(\"plot_mae_num_tree_sorted:\",plot_mae_num_tree_sorted)\n\nmy_num_tree=plot_mae_num_tree_sorted[0][0]   ## \u8bef\u5dee\u6700\u5c0f\u7684\u51b3\u7b56\u6811\u4e2a\u6570\nprint(\"my_num_tree:\",my_num_tree)", "execution_count": 136, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<matplotlib.figure.Figure at 0x7fc65ca37390>", "image/png": 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\n"}, "metadata": {}}, {"output_type": "stream", "text": "plot_mae_num_tree_sorted: [(39, 0.4117988782051281), (27, 0.4154513888888891), (36, 0.41589640696068886), (44, 0.4168335700757577), (45, 0.41758838383838376), (32, 0.422314453125), (46, 0.42264996617739714), (23, 0.4232676630434783), (34, 0.42450776143790847), (49, 0.42463860544217696), (40, 0.4249528945324196), (25, 0.42511607142857155), (31, 0.4253024193548386), (43, 0.42532262472669463), (41, 0.42540650406504066), (26, 0.4262219551282052), (35, 0.42647321428571433), (37, 0.42726914414414424), (47, 0.42796364167099094), (30, 0.4286458333333332), (48, 0.42940484614928404), (19, 0.4297971491228072), (42, 0.4303571428571429), (38, 0.4307565789473686), (14, 0.4314732142857142), (28, 0.4317362882653063), (17, 0.4324632352941175), (33, 0.43300189393939414), (18, 0.4334143518518519), (24, 0.4338541666666666), (9, 0.4343749999999999), (22, 0.43494318181818203), (29, 0.4349799615230399), (12, 0.4361979166666668), (16, 0.437890625), (13, 0.43810096153846184), (20, 0.4408072916666665), (21, 0.4413465007215006), (11, 0.4423295454545455), (10, 0.44281250000000016), (15, 0.44329166666666653), (8, 0.44453125), (7, 0.4522321428571428), (4, 0.45234375), (6, 0.45657169117647045), (3, 0.4614583333333332), (5, 0.4868750000000001), (2, 0.4984375)]\nmy_num_tree: 39\n", "name": "stdout"}]}, {"metadata": {}, "cell_type": "markdown", "source": "### 3.4.2 \u5bf9\u6700\u5927\u6df1\u5ea6\u8c03\u53c2\n\u6211\u4eec\u53ef\u4ee5\u4ece\u4e0a\u9762\u5f97\u5230\uff0c\u5f53\u6700\u5927\u6df1\u5ea6\u4e3a20\u65f6\uff0c\u6211\u4eec\u53ef\u4ee5\u5f97\u5230\uff0c\u51b3\u7b56\u6811\u4e2a\u6570\u4e3a my_num_tree \u65f6\uff0c\u9a8c\u8bc1\u96c6\u7edd\u5bf9\u5e73\u5747\u8bef\u5dee\u6700\u5c0f\uff0c\u6240\u4ee5\u6211\u4eec\u5c06\u51b3\u7b56\u6811\u4e2a\u6570\u5b9a\u4f4d my_num_tree \uff0c\u7136\u540e\u518d\u4ece\uff082,30\uff09\u8303\u56f4\u5185\u641c\u7d22\u6700\u5927\u6df1\u5ea6\u3002"}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "plot_mae_num_adh = {}\n\nfor num_adh  in range(2,30):\n    my_mae = get_mae_rf(my_num_tree,num_adh,label_features,inputcols,val_data_)   #\u8fd9\u91ccmy_num_tree\u662f39\n    plot_mae_num_adh[num_adh] = my_mae\n    \nplt.plot(list(plot_mae_num_adh.keys()),plot_mae_num_adh.values())\nplt.xlabel(\"Iter\")\nplt.ylabel(\"Mean Absolute Deviation \")\nplt.show()\nplot_mae_num_adh_sorted = sorted(plot_mae_num_adh.items(),key=itemgetter(1))\nprint(\"plot_mae_num_adh_sorted\",plot_mae_num_adh_sorted)\n\nmy_num_adh=plot_mae_num_adh_sorted[0][0]\nprint(\"my_num_adh:\",my_num_adh)", "execution_count": 137, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<matplotlib.figure.Figure at 0x7fc65f7e93c8>", "image/png": 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\n"}, "metadata": {}}, {"output_type": "stream", "text": "plot_mae_num_adh_sorted [(19, 0.411759421134421), (21, 0.41177884615384597), (22, 0.41177884615384597), (23, 0.41177884615384597), (24, 0.41177884615384597), (25, 0.41177884615384597), (26, 0.41177884615384597), (27, 0.41177884615384597), (28, 0.41177884615384597), (29, 0.41177884615384597), (20, 0.4117988782051281), (18, 0.4121051215522368), (17, 0.41218599637296605), (16, 0.4127107873881036), (15, 0.4130998769179529), (14, 0.4155194204337077), (13, 0.4188849939968137), (12, 0.4205657097851569), (11, 0.42483534887334307), (10, 0.4291626991887297), (9, 0.43577159509640706), (8, 0.4453523896189984), (7, 0.4614786759266961), (6, 0.473745694589269), (5, 0.49075113586572605), (4, 0.5117019934734286), (3, 0.5342361280792266), (2, 0.5636701252640647)]\nmy_num_adh: 19\n", "name": "stdout"}]}, {"metadata": {}, "cell_type": "markdown", "source": "### 3.4.3\u786e\u5b9a\u53c2\u6570\n\u53ef\u4ee5\u770b\u5230 \u51b3\u7b56\u6811\u4e2a\u6570\u4e3a my_num_tree \uff0c\u6700\u5927\u6df1\u5ea6\u4e3a my_num_adh \u65f6\uff0c\u6709\u6700\u5c0f\u8bef\u5dee\uff0c\u6240\u4ee5\u6211\u4eec\u6700\u540e\u7684\u53c2\u6570\u5b9a\u4e3a numTrees =my_num_tree, maxDepth =my_num_adh\u3002\n\u53e6\u5916\u6211\u8fd9\u91cc\u662f\u51b3\u7b56\u6811\u4e2a\u6570\u548c\u6700\u5927\u6df1\u5ea6\u5206\u522b\u8fdb\u884c\u9009\u62e9\uff0c\u4e5f\u53ef\u4ee5\u7528\u7f51\u683c\u641c\u7d22\u6cd5\uff0c\u5c06\u8fd9\u4e24\u4e2a\u503c\u4e24\u4e24\u7ec4\u5408\u4e3a\u4e0d\u540c\u7684\u7ec4\u5408\uff0c\u7136\u540e\u5224\u65ad\u54ea\u79cd\u7ec4\u5408\u9a8c\u8bc1\u96c6\u8bef\u5dee\u6700\u5c0f\uff0c\u4f46\u662f\u8fd9\u79cd\u65b9\u6cd5\u80af\u5b9a\u66f4\u8d39\u65f6\u95f4\uff0c\u6709\u5174\u8da3\u7684\u8bfb\u8005\u53ef\u4ee5\u4e0b\u53bb\u81ea\u5df1\u8c03\u8bd5\u3002"}, {"metadata": {}, "cell_type": "markdown", "source": "## 3.5\u6a21\u578b\u786e\u5b9a\u540e\u91cd\u65b0\u8bad\u7ec3\uff0c\u5e76\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8fdb\u884c\u6d4b\u8bd5\n\u4e0a\u9762\u6211\u4eec\u786e\u5b9a\u4e86\u51b3\u7b56\u6811\u548c\u6700\u5927\u6df1\u5ea6\uff0c\u5e76\u628a\u5b83\u4eec\u5206\u522b\u4fdd\u5b58\u5728\u4e86 my_num_tree\u548cmy_num_adh\u4e2d\uff0c\u786e\u5b9a\u53c2\u6570\u540e\u91cd\u65b0\u8bad\u7ec3\u6a21\u578b\u4f5c\u4e3a\u6700\u540e\u7684\u6a21\u578b\u3002\u5e76\u89c2\u6d4b\u6d4b\u8bd5\u96c6\u8bef\u5dee\u662f\u591a\u5c11\u3002"}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "RFR = RandomForestRegressor(numTrees =my_num_tree, maxDepth =my_num_adh)\n\ninputcols=[\"fixed acidity\", \"volatile acidity\",\"citric acid\",\n                                                             \"chlorides\",\"free sulfur dioxide\",\"total sulfur dioxide\",\"density\",\n                                                             \"pH\",\"sulphates\",'alcoho']\nassembler = pyspark.ml.feature.VectorAssembler(inputCols=inputcols, outputCol='features')\noutput = assembler.transform(train_data_)\nlabel_features = output.select(\"features\", output.quality.cast('double')).toDF('features','label')\n\n\nRFRModel = RFR.fit(label_features)\n\n\nmetric_dict_val=calculate_metric_value(RFRModel, val_data_,inputcols) # \u9a8c\u8bc1\u96c6\u8bef\u5dee\nprint(\"val mae:\",metric_dict_val)\nmetric_dict_test=calculate_metric_value(RFRModel, test_data_,inputcols)   #\u6d4b\u8bd5\u96c6\u8bef\u5dee\nprint(\"test mae:\",metric_dict_test)\n", "execution_count": 138, "outputs": [{"output_type": "stream", "text": "val mae: {'mae': 0.411759421134421}\ntest mae: {'mae': 0.43901705932955937}\n", "name": "stdout"}]}, {"metadata": {}, "cell_type": "markdown", "source": "### \u5bf9\u6bd4\u4e00\u4e0b\u6ca1\u8fdb\u884c\u8fc7\u8c03\u4f18\u7684\u6a21\u578b\u5728\u6d4b\u8bd5\u96c6\u4e0a\u7684\u8bef\u5dee\n\u53ef\u4ee5\u770b\u5230\uff0c\u8c03\u8fc7\u4f18\u7684\u6a21\u578b\u6d4b\u8bd5\u96c6\u7684\u8bef\u5dee\u663e\u7136\u66f4\u5c0f"}, {"metadata": {"trusted": true}, "cell_type": "code", "source": "from numpy import allclose\nfrom pyspark.ml.linalg import Vectors\n\nRFR = RandomForestRegressor()\n\ninputcols=[\"fixed acidity\", \"volatile acidity\",\"citric acid\",'residual sugar',\n                                                             \"chlorides\",\"free sulfur dioxide\",\"total sulfur dioxide\",\"density\",\n                                                             \"pH\",\"sulphates\",'alcoho']\nassembler = pyspark.ml.feature.VectorAssembler(inputCols=inputcols, outputCol='features')\noutput = assembler.transform(train_data_)\nlabel_features = output.select(\"features\", output.quality.cast('double')).toDF('features','label')\n\n\nRFRModel = RFR.fit(label_features)\n\nmetric_dict_val=calculate_metric_value(RFRModel, val_data_,inputcols) # \u9a8c\u8bc1\u96c6\u8bef\u5dee\nprint(\"val mae:\",metric_dict_val)\nmetric_dict_test=calculate_metric_value(RFRModel, test_data_,inputcols) #\u6d4b\u8bd5\u96c6\u8bef\u5dee\nprint(\"test mae:\",metric_dict_test)", "execution_count": 139, "outputs": [{"output_type": "stream", "text": "val mae: {'mae': 0.49778909960545314}\ntest mae: {'mae': 0.4810245253751081}\n", "name": "stdout"}]}], "metadata": {"kernelspec": {"name": "pyspark-2.3.2", "display_name": "PySpark-2.3.2", "language": "python"}, "language_info": {"name": "python", "version": "3.6.4", "mimetype": "text/x-python", "codemirror_mode": {"name": "ipython", "version": 3}, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py"}}, "nbformat": 4, "nbformat_minor": 2}